INTRODUCTION: Bleeding risk assessment plays a critical role in anticoagulation management for atrial fibrillation (AF), to balance stroke prevention with risk of major hemorrhage. Traditional bleeding risk models, such as HAS-BLED, ORBIT, and ATRIA, offer valuable insights but have limitations in predictive accuracy and clinical applicability. Recent advances in risk stratification have introduced novel models integrating biomarkers, genetic data, and artificial intelligence (AI)-driven algorithms to improve precision and individualized patient care.

AREAS COVERED: This review evaluates strengths and limitations of established bleeding risk assessment tools and explores emerging trends in predictive modeling. It discusses novel risk stratification models- DOAC Score, GARFIELD-AF, and HEMORR₂HAGES, which incorporate renal function markers, hematologic parameters, and genetic polymorphisms to enhance predictive accuracy. Integration of machine learning and digital health tools, such as the Universal Clinician Device (UCD) and the mAFA-II mobile application, was also examined for their role in improving anticoagulation safety and adherence.

EXPERT OPINION: The future of bleeding risk assessment lies in AI-driven, real-time risk prediction models adapting to dynamic patient profiles. Enhanced integration of digital health solutions and learning health systems will minimize adverse events while optimizing stroke prevention. Future research should prioritize the validation and standardization of these novel tools.